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ML, GenAI, and serving on the lakehouse — Databricks Mosaic AI, in production.
When your work centers on machine learning, live streaming data, messy free-form data (images, audio, documents), or further-training AI models on your own data (called fine-tuning), Databricks is usually the right target. We build MLflow (which keeps track of every model and result), a shared, reusable library of the data your models rely on (a Feature Store), live model serving, Vector Search and an AI assistant that answers from your own documents (RAG), and Mosaic AI fine-tuning — with the controls, a test set that checks the AI is actually right, and the written guides your team needs to run it themselves.
What this means for you
If your business depends on prediction models, real-time decisions, or AI trained on your own data, this is about running all of it reliably in one place your team can actually manage — instead of a patchwork of stitched-together tools no one fully owns.
Storing data, preparing it, training models, and running them — all in one place.
A modern cloud data platform like this keeps your data, your model inputs, and your models together instead of constantly shuttling data between them — which matters when you're running machine learning at scale, working with live streaming data, or planning to further-train AI models on your own data.
Databricks is the right answer when your work leans heavily on machine learning (training and retraining models at scale), on messy free-form data (images, audio, logs, documents), on live streaming data, or on further-training open AI models on your own data (called fine-tuning) where that pays off. Mosaic AI gives you fine-tuning, live model serving, and a search-by-meaning index that sits on the same Delta tables (Databricks' core data tables) as your training data — with Unity Catalog (its built-in security and governance) controlling everything. We've shipped all of it for mid-sized teams. The platform isn't always the right answer; for work that's close to everyday reporting, or where security and control matter most, Snowflake Cortex often wins. We're transparent about which.
The Mosaic AI stack we ship most often.
Each piece sits on the Databricks setup you already run, with Unity Catalog (Databricks' built-in security and governance) controlling the whole thing.
MLflow to track and manage every model
Keeps a record of every experiment, a catalog of your models and which version is live, a trace of each step your AI assistant takes, and the audit trail that lets you reproduce any result. Anchored in Unity Catalog so security and governance stay consistent.
A shared, reusable library of the data your models rely on
The inputs your models use are defined once, in one place (this is what people call a Feature Store), and reused everywhere — both when training a model and when running it live. This prevents a common, silent failure where a model is fed slightly different data in testing than it gets in real use, and it lets several models safely share the same inputs.
Model Serving for fast, reliable answers
Running your models live with guaranteed response times — steady capacity for predictable demand, on-demand capacity for spiky demand, the ability to test two versions head-to-head, roll out a new version gradually and roll it back instantly, and a log of every prediction. Response-time guarantees the business can rely on.
Vector Search built into the same platform
A managed search-by-meaning index (the part that lets the AI search your documents by meaning) that sits on the same Delta tables as your everyday business data. It updates automatically whenever the source data changes. Your AI assistant can combine what it finds with your structured business data in a single step, without leaving the platform.
Mosaic AI fine-tuning and serving
Further-train open AI models (Llama, Mistral, DBRX) on your own data with managed training jobs, then run them through the same Model Serving setup. Worth it mainly for specialized language, for getting reliable, consistent output, or for cutting the cost per use at very high volume.
Unity Catalog for security, traceability, and monitoring
Your tables, model inputs, models, search indexes, and live models are all governed in one place. You can trace any number all the way from raw data to model output. Audit trails and access rules are enforced everywhere.
Mosaic AI use cases we ship most often.
Patterns where this all-in-one platform pays off.
Customer 360 + propensity / churn / fraud / LTV
The shared data library, MLflow, and live model serving cover the whole lifecycle — from scoring which customers might leave or buy, to spotting fraud. Fast enough (under a tenth of a second) to score fraud and recommendations in real time.
An AI assistant over large or fast-changing document sets
Vector Search updates itself automatically as your Delta tables change. Useful when you have a lot of documents, they change often, and it helps to combine search-by-meaning results with your structured business data in a single step.
Models trained on your industry's language
Open AI models further-trained on your own documents, run through Mosaic AI. We do this only when the business case justifies it — usually specialized language, the need for reliable, consistent output, or cutting the cost per use at very high volume.
Streaming and real-time ML
Live streaming data, the shared data library, and live model serving together, for cases where up-to-the-second freshness matters: real-time fraud, pricing that changes on the fly, and personalization in the moment.
When Databricks isn't the right answer.
Transparent about where the platform fits.
Your data and security setup is Snowflake-first
If your data, your security setup, and your team's skills are deeply on Snowflake, dragging the AI work over to Databricks adds friction. Cortex is usually the better answer in that situation.
Your work is mostly everyday reporting
If you're not training models, not further-training AI on your own data, not handling live streaming, and not working with much messy free-form data, the advantage of this platform is smaller. A Snowflake-based or mixed setup often serves better.
No appetite to manage the platform day to day
Databricks rewards teams who invest in running the platform well. Teams without the bandwidth to manage it carefully — the access rules, the scheduled jobs, the Unity Catalog setup — can struggle. We can run that for you, but it's a real commitment.
The foundation isn't there yet
Mosaic AI inherits whatever's underneath it. Without Unity Catalog set up properly, agreed definitions for your model inputs, and each customer, vendor, or product showing up as one clean record, the AI layer just spreads the underlying mess.
Three reversible phases.
Same engagement model as the rest of our work.
See the full engagement modelMosaic AI readiness assessment
Quoted by scopeWe review your Databricks setup, your Unity Catalog security, and the use cases on your roadmap. You leave with a recommended design, a plan for each use case, and a cost estimate.
Foundation & first use case
Quoted by scopeTightening up Unity Catalog security, setting up the shared data library, MLflow and live model serving, and Vector Search wherever a document assistant is in scope. The first use case shipped end-to-end on that foundation.
Scale & enablement
Quoted by scopeMore models and AI work on the same platform, working side by side with your team on the written guides. Post-launch and ongoing support are available by quote.
The questions we get most often.
Mosaic AI, or building our own setup on Databricks?
Mosaic AI is the ready-made bundle: MLflow, the shared data library, live model serving, Vector Search, and fine-tuning all wired together under Unity Catalog's security. You can absolutely build your own (many teams have); the question is whether your team wants to maintain all the connecting pieces forever. For most mid-sized teams, the ready-made bundle costs less overall.
Do you fine-tune by default?
No — almost every time we start with a leading paid AI service or an off-the-shelf open model. Further-training a model on your own data earns its keep when your industry's language is unusual, when you need very reliable, consistent output, or when the cost per use at very high volume justifies the extra work. The readiness check makes the call candidly.
How does Vector Search compare to Pinecone or pgvector?
Vector Search's advantage is staying in one place: the search-by-meaning index lives on your Delta tables, updates itself as the data changes, and its results can be combined with your structured business data in a single step. The trade-off is being tied to the platform and slightly less control than a dedicated tool like Pinecone or pgvector. When your documents and your business data already live in Databricks, Vector Search is usually the right default.
What about Snowflake at the same time?
A common pattern at mid-sized companies: Snowflake for everyday reporting, Databricks for machine learning and AI work. We connect the two cleanly and securely, and don't force-fit toward one or the other.
Are you a Databricks partner?
We work extensively on the Databricks stack and ship Mosaic AI builds regularly. We stay deliberately independent on recommendations — when Snowflake Cortex or a mixed setup is the better answer, we'll say so.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.